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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2021 / Cilt: 23 - Sayı: 68

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

Etiketlenmiş Verilerin Kıtlığı Durumunda Suç Oluşumunun Tahmini

Volkan KIRANOGLU
Göksu TÜYSÜZOĞLU
Elife ÖZTÜRK KIYAK
Pages
677–687
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis.
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Key Findings & Data:By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily.
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Conclusion & Impact:According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.
🔬Design:Modeling & Simulation
📍Scope:2019 Verileri
⚙️Variables:Aktif Öğrenme · Sınıflandırma · Suç Tespiti

Abstract

In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis. The prediction of crime occurrences, the detection of the spatial/temporal distribution of the criminal cases, forecasting the type of crime are some of these study areas. By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily. Therefore, in this study, active learning, self-learning, and random sampling techniques are applied to predict the outcome of criminal searches in England using the police data of 2019. According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.

Özet

Teknolojik gelişmeler doğrultusunda, makine öğrenmesi/veri madenciliği çalışmaları suç analizinde önemli ölçüde artmıştır. Suç olaylarının tahmini, ceza davalarının mekansal/zamansal dağılımının tespiti, suç türünün öngörülmesi bu çalışma alanlarından bazılarıdır. Suç oranlarındaki önemli artıştan kaynaklanan suç verileri dikkate alındığında, gelecekteki olaylar için suç kalıplarını araştırmak veya suçla ilgili tahminleri kolayca yapmak için etiketlenmemiş veriler kullanılabilir. Bu nedenle, bu çalışmada, 2019 polis verilerini kullanarak İngiltere'de suç araştırmalarının sonucunu tahmin etmek için aktif öğrenme, kendi kendine öğrenme ve rastgele örnekleme teknikleri uygulanmıştır. Deneysel analize göre, aktif öğrenme, çok az etiketlenmiş veri olması durumunda düzensizliğe dayalı akıllı seçim stratejisini kullanarak muadillerinden daha iyi performans göstermektedir.

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Cite This Article

KIRANOGLU, V., TÜYSÜZOĞLU, G., KIYAK, E. Ö. (2021). Prediction of Crime Occurrence in case of Scarcity of Labeled Data. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 23 - Sayı: 68, 677–687. https://ajindex.com/articles/348353

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References (7)

  1. Shukla, S., Jain, P.K., Babu, C.R., Pamula, R. 2020. A Multivariate Regression Model for Identifying, Analyzing and Predicting Crimes, Wireless Personal Communications. DOI: 10.1007/s11277-020-07335-w Kadar, C., Pletikosa, I. Mining Large-Scale Human Mobility Data for Long-Term Crime Prediction 2018.
  2. EPJ Data Science, Volume. 7(26). DOI: 10.1140/epjds/s13688-018-0150-z Agrawal, S., Sejwar, V. 2017. Crime Identification using FP-Growth and Multi Objective Particle Swarm Optimization. IEEE International Conference on Trends in Electronics and Informatics, 11-12 May 2017,
  3. Tirunelveli, India, 727-734. DOI: 10.1109/ICOEI.2017.8300799 Nitta, G.R., Rao, B.Y., Sravani, T., Ramakrishiah, N., BalaAnand, M. 2019. LASSO-Based Feature Selection and Naïve Bayes Classifier for Crime Prediction and Its Type, Service Oriented Computing and Applications, Volume. 13(3), p. 187-197. DOI: 10.1007/s11761-018-0251-3
  4. Tayal, D.K., Jain, A., Arora, S., Agarwal, S., Gupta, T., Tyagi, N. 2015. Crime Detection and Criminal Identification in India using Data Mining Techniques, AI & Society, Volume. 30(1), p. 117-127. DOI: 10.1007/s00146-014-0539-6
  5. Shermila, A.M., Bellarmine, A.B., Santiago, N. 2018. Crime Data Analysis and Prediction of Perpetrator Identity using Machine Learning Approach. IEEE 2nd International Conference on Trends in Electronics and Informatics, 11-12 May 2018,
  6. Tirunelveli, India, 107-114. DOI: 10.1109/ICOEI.2018.8553904 Khan, J., Lee, Y.K. 2019, LeSSA: A Unified Framework Based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification, Applied Sciences, Volume. 9, p. 5562-5590. DOI: 10.3390/app9245562 Iqbal, R., Murad, M.A.A., Mustapha, A., Panahy, P.H.S., Khanahmadliravi, N. 2013. An Experimental Study of Classification Algorithms for Crime Prediction, Indian Journal of Science and Technology, Volume. 6(3), p. 4219-4225. DOI: 10.17485/ijst/2013/v6i3.6 Nguyen, T.T., Hatua, A., Sung, A.H. 2017. Building a Learning Machine Classifier with Inadequate Data for Crime Prediction. Journal of Advances in Information Technology, Volume. 8(2), p. 141-147. DOI: 10.12720/jait.8.2.141-147
  7. Alves, L.G., Ribeiro, H.V., Rodrigues, F.A. 2018. Crime Prediction through Urban Metrics and Statistical Learning. Physica A: Statistical Mechanics and its Applications, Volume. 505, p. 435-443. DOI: 10.1016/j.physa.2018.03.084 Chhabra G., Vashisht V., Ranjan J., Crime Prediction Patterns Using Hybrid K-Means Hierarchical Clustering. Journal of Advanced Research i